Title

Scale-free networks in metabolomics

Authors

Hema Sekhar Reddy Rajula^1,2*, Matteo Mauri³, Vassilios Fanos¹

Affiliation

¹Neonatal Intensive Care Unit, Department of Surgical Sciences, Neonatal Pathology and Neonatal Section, University of Cagliari, Cagliari, Italy;

²PhD student Marie Sklodowska-Curie CAPICE Project;

³University of Cagliari, Cagliari, Italy;

Email

hemasekhar09@gmail.com;

Article Type

Views

Date

Received February 19, 2018; Revised March 28, 2018; Accepted March 1, 2018; Published March 31, 2018

Metabolomics is an expanding discipline in biology. It is the process of portraying the phenotype of a cell, tissue or species organism using a comprehensive set of metabolites. Therefore, it is of interest to understand complex systems such as metabolomics using a scale-free topology. Genetic networks and the World Wide Web (WWW) are described as networks with complex topology. Several large networks have vertex connectivity that goes beyond a scale-free power-law distribution. It is observed that (a) networks expand constantly by the addition of recent vertices, and (b) recent vertices attach preferentially to sites that are already well connected. Scalefree networks are determined with precision using vital features such as a structure, a disease and a patient. This is pertinent to the understanding of complex systems such as metabolomics. Hence, we describe the relevance of scale-free networks in the understanding of metabolomics in this article.

Keywords

metabolomics, scale-free networks, complex systems, pathways, modelling, metabolites

Citation

Rajula et al. Bioinformation 14(3): 140-144 (2018)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.